A New "Baby X"

Parents keep child’s gender under wraps

http://news.yahoo.com/s/yblog_thelookout/20110524/ts_yblog_thelookout/parents-keep-childs-gender-under-wraps

I know this may be old news by now (posted May 24) but I still can’t get the message it carries out of my mind. I highly recommend reading the article I posted on the top, but for a quick summary: Two parents decided NOT to reveal their healthy baby’s gender and this has essentially caused an uproar from family, friends, etc. For those of you who remember Baby X (http://www.sa.ucsb.edu/women/blog/file.axd?file=2011%2F4%2FBabyX.pdf) people don’t know to do when they cannot approach a baby and say “Oh, she’s a doll!” or “What a handsome boy!” Is it that unacceptable to simply acknowledge a healthy, beautiful new being in the world?

I’ve focused some of my studying in gender issues but I have not delved into the subject nearly as deeply as I would like to, so please excuse and feel free to correct me if I begin to over-generalize. I believe there is something inherently wrong with the way gender has been constructed in our society. And sadly, I cannot seem to put my finger on exactly what that is.

Gender is no longer as simple as, “If it has a penis, it’s a boy.” Genitalia doesn’t tell gender and neither do hormones, physical attributes, neurons… There is no universal “if you have this, then you’re this.” I hate to say it, but I am morally dumbfounded to even find an appropriate definition of gender.

I believe we have a few options to ameliorate our current dependance on gender categorization. I think a good first step is to proliferate the categories: add a checkbox on the SATs for intersex, male-identified female, female-identified male, unkown sex, etc. The list goes on and on. Think though, would this not be an excellent way of educating the public and making aware the fact that gender is by no means a binary system? However, I can’t help but see this eventually going too far… Refer to the “one drop rule” of race: “I’m one drop Irish, one drop German, one drop South African, one drop Russian….” There’s no end to the combinations people can conjure once this option is offered. Once people are aware that there are other options besides White or Black, they cannot help but start to think of all the ways they now fit under the “Other” category. Gender proliferation, therefore, would be a great step to induce awareness of the problem, but as far as solving it goes… I just don’t see it.

That leads me to a quasi-eliminativist approach. (For further information on this, please look to Laurie Shrage’s “Sex and Miscibility” where I agree with much of her argument). What if we could eliminate the gender binary system to the extent that it was only on a medically need-to-know basis. Other than that, humans were free to be and love whatever they chose. A good friend of mine uses the term “sexual fluidity” and I’d like to apply that to gender as well. There would be no gay, lesbian, bi-sexual, trans-sexual; there would be humans and love would be fluid, so to speak.

Sound nice? What about transsexuals, though? The people who enjoy a sort of dual-identity; the ones who need categories to be established for them to enjoy their category. With quasi-eliminativism, transsexuals lose this identity, because there would be no need to classify someone as a female in male’s body, or vis versa. Also, I wonder if we eliminated gender identities, would we begin to lose other forms of categorization? Such as hierarchies in the workplace or support groups for certain kinds of people/causes (would there be a women’s movement?). What would happen to reproduction if we could no longer preach to find the opposite sex and form a family? I don’t have an answer for this either.

This all brings me back to baby Storm. Storm’s parents seem to be acknowledging the gender system as current standards define it, but they are letting Storm choose his or her or its place. As one of Storm’s parents so eloquently put it, "If you really want to get to know someone, you don’t ask what’s between their legs."

I can’t wait to follow up on this piece. I imagine the world a better place without these kind of pressures. Without being dressed in pink or blue the second you’re born, or handed dolls and play kitchens vs toy trucks and baseballs. There is nothing wrong with ambiguity, it does not matter if you walk up to a person and cannot immediately tell “boy” or “girl.” We’re humans, and what’s between our legs should not give us our identities before we can choose them.

Successful innovation is more of an act of empathy than an act of imposition. If you look at sustainable innovation cultures, what you are seeing are people who really try to align innovation with individual and institutional transformation.
Collaborative Working in Teams

Collaborative Working in Teams

The idea behind collaboration is simple; people are more effective, learn better and achieve more when they work in groups rather than on their own.

The term collaboration is used widely and often synonymously with other terms such as cooperation and coordination. However Schrage (1990) makes an important distinction between those terms.

Communicationis the exchange of information. In order to be…

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Ins Gebet kommen

Die vorliegende Sammlung von traditionellen und neuen Gebeten lädt ältere Menschen, ihre Angehörigen und das Pflegepersonal ein, gemeinsam mit Gott ins Gespräch zu kommen. Die Karten zu Themen wie “Loslassen”, “Ermutigung” und “Dank” sind eine ideale Sammlung für Pflegebedürftige, ihre Angehörigen und Mitarbeiterinnen und Mitarbeiter in der Pflege.
Gebete in Krankheit, Pflege und Alter
durchgehend vierfarbig gestaltet

  • Auflage: 1
  • Seitenzahl: 56
  • Autoren / Künstler: Schrage, Brune / Fischer-Wolff, Karola (Hg.)
  • Ausstattung: Falschachtel
  • Material:
  • Groesse / Format: 13 x 9 cm
Failure Is The Key To Learning From Big Data

Just as the traditional sequential (and slow) “waterfall" approach to software development has started to fade into disrepute, a similar approach to Big Data analytics has surfaced. For too many organizations, devising incredibly potent models for predicting behavior is becoming an end in itself, obscuring the process by which we learn from our data.

Indeed, much of the focus on Big Data lies in accumulating more and more data to improve our ability to predict what consumers will buy, which customers will churn, etc. And so we put inordinate amounts of effort into perfecting our prediction models rather than learning from their many failures to do so.

Unfortunately, our data infrastructure too often gets in the way of our ability to embrace failure, which is why the cloud is so important to Big Data.

The Process Of Prediction

As Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, stresses:

[The] most enduring impact of predictive analytics … comes less from quantitatively improving the quality of prediction than from dramatically changing how organizations think about problems and opportunities.

In other words, if we’re paying attention, the process can help us “better understand[] the real business challenges [our] predictive analytics address.”

But to do this well, we need to be willing to fail. Again. And again. As Schrage notes:

Ironically, the greatest value from predictive analytics typically comes more from their unexpected failures than their anticipated success. In other words, the real influence and insight come from learning exactly how and why your predictions failed. Why? Because it means the assumptions, the data, the model and/or the analyses were wrong in some meaningfully measurable way.

Failure, then, is the key to learning from Big Data. Hadoop vendor Cloudera rightly challenges us to “ask bigger questions,” but a key component of these questions is iterating through trial-and-error toward the right questions to ask. 

Institutionalizing Failure In The Cloud

While a cloud environment won’t kill a company’s fixation with Big Models for Big Data, it sets the appropriate tone for experimentation. Big data is all about asking the right questions. Hence the importance of domain knowledge. 

This is why I keep coming back to Gartner analyst Svetlana Sicular’s contention that “Learning Hadoop is easier than learning the company’s business,” which means that the first place to look for Big Data expertise is in-house, not the land of magical data-science fairies. 

Even so, no matter how smart you or your data-science team is, your initial questions are almost certainly going to be wrong. In fact, you’ll probably fail to collect the right data and to ask pertinent questions—over and over again. 

As such, it’s critical to use a flexible, open data infrastructure that allows you to continually tweak your approach until it bears real fruit.

In a conversation I had with Matt Wood (@mza), general manager of data science at Amazon Web Services, he describes just how hard it is to approach data correctly when our hardware and software infrastructure gets in the way:

Those that go out and buy expensive infrastructure find that the problem scope and domain shift really quickly. By the time they get around to answering the original question, the business has moved on. You need an environment that is flexible and allows you to quickly respond to changing big data requirements. Your resource mix is continually evolving—if you buy infrastructure it’s almost immediately irrelevant to your business because it’s frozen in time. It’s solving a problem you may not have or care about any more.

Cloud, in other words, is all about creating a culture that can iterate without fear of failure. 

All Your Big Data Are Belong To The Cloud

This isn’t to suggest that cloud obviates failure. Quite the contrary. As Wood says, it’s all about making the cost of failure acceptable: “You’re going to fail a lot of the time, and so it’s critical to lower the cost of experimentation.”

It’s also not to suggest that Big Data projects will only succeed in the cloud. As Shaun Connolly, vice president of Strategy at Hortonworks, a leading Hadoop vendor, told me:

I believe there will be multiple centers of data gravity, one of which is on-premises. But I am convinced Hadoop in the cloud plays a significant role in the broader architecture as the Hadoop market continues to mature.

In sum, Big Data doesn’t have to be in the cloud, and for many workloads it may make sense to store, process and analyze the data on-premise. But for building a culture of experimentation, the essence of Big Data discovery, cloud is critical.

Image courtesy of Shutterstock



from ReadWrite http://j.mp/1rG4ASy
Failure Is The Key To Learning From Big Data

Just as the traditional sequential (and slow) “waterfall" approach to software development has started to fade into disrepute, a similar approach to Big Data analytics has surfaced. For too many organizations, devising incredibly potent models for predicting behavior is becoming an end in itself, obscuring the process by which we learn from our data.

Indeed, much of the focus on Big Data lies in accumulating more and more data to improve our ability to predict what consumers will buy, which customers will churn, etc. And so we put inordinate amounts of effort into perfecting our prediction models rather than learning from their many failures to do so.

Unfortunately, our data infrastructure too often gets in the way of our ability to embrace failure, which is why the cloud is so important to Big Data.

The Process Of Prediction

As Michael Schrage, a research fellow at MIT Sloan School’s Center for Digital Business, stresses:

[The] most enduring impact of predictive analytics … comes less from quantitatively improving the quality of prediction than from dramatically changing how organizations think about problems and opportunities.

In other words, if we’re paying attention, the process can help us “better understand[] the real business challenges [our] predictive analytics address.”

But to do this well, we need to be willing to fail. Again. And again. As Schrage notes:

Ironically, the greatest value from predictive analytics typically comes more from their unexpected failures than their anticipated success. In other words, the real influence and insight come from learning exactly how and why your predictions failed. Why? Because it means the assumptions, the data, the model and/or the analyses were wrong in some meaningfully measurable way.

Failure, then, is the key to learning from Big Data. Hadoop vendor Cloudera rightly challenges us to “ask bigger questions,” but a key component of these questions is iterating through trial-and-error toward the right questions to ask. 

Institutionalizing Failure In The Cloud

While a cloud environment won’t kill a company’s fixation with Big Models for Big Data, it sets the appropriate tone for experimentation. Big data is all about asking the right questions. Hence the importance of domain knowledge. 

This is why I keep coming back to Gartner analyst Svetlana Sicular’s contention that “Learning Hadoop is easier than learning the company’s business,” which means that the first place to look for Big Data expertise is in-house, not the land of magical data-science fairies. 

Even so, no matter how smart you or your data-science team is, your initial questions are almost certainly going to be wrong. In fact, you’ll probably fail to collect the right data and to ask pertinent questions—over and over again. 

As such, it’s critical to use a flexible, open data infrastructure that allows you to continually tweak your approach until it bears real fruit.

In a conversation I had with Matt Wood (@mza), general manager of data science at Amazon Web Services, he describes just how hard it is to approach data correctly when our hardware and software infrastructure gets in the way:

Those that go out and buy expensive infrastructure find that the problem scope and domain shift really quickly. By the time they get around to answering the original question, the business has moved on. You need an environment that is flexible and allows you to quickly respond to changing big data requirements. Your resource mix is continually evolving—if you buy infrastructure it’s almost immediately irrelevant to your business because it’s frozen in time. It’s solving a problem you may not have or care about any more.

Cloud, in other words, is all about creating a culture that can iterate without fear of failure. 

All Your Big Data Are Belong To The Cloud

This isn’t to suggest that cloud obviates failure. Quite the contrary. As Wood says, it’s all about making the cost of failure acceptable: “You’re going to fail a lot of the time, and so it’s critical to lower the cost of experimentation.”

It’s also not to suggest that Big Data projects will only succeed in the cloud. As Shaun Connolly, vice president of Strategy at Hortonworks, a leading Hadoop vendor, told me:

I believe there will be multiple centers of data gravity, one of which is on-premises. But I am convinced Hadoop in the cloud plays a significant role in the broader architecture as the Hadoop market continues to mature.

In sum, Big Data doesn’t have to be in the cloud, and for many workloads it may make sense to store, process and analyze the data on-premise. But for building a culture of experimentation, the essence of Big Data discovery, cloud is critical.

Image courtesy of Shutterstock



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Halston Sage hot pictures

Halston Sage hot pictures

Born: May 10, 1993,Los Angeles, California, United States Movies: The First Time Halston Jean Schrage  also known by her stage name Halston Sage, is an American actress and singer, known for her role as Grace on the Nickelodeon TV series How to Rock. Sage was born in Los Angeles, California. Born to Lenny and Tema Sage, she has two younger siblings, Max and Kate Sage. Her career began in 2011,…

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You can only guess what was said… Barly’s spawn. #catsofinstagram #cat #kitten not mans best friend I don’t care going to work. 😫🙈🏃💬”don’t buy cat food or cat litter it’s not staying!” (at Schrage House)

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